Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. In image classification, we found that humans adopted more explorative attention strategies for explanation than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations diagnostic for inferring class labels, whereas the other involved explorative scanning with more visual explanations rated higher for effectiveness. Interestingly, XAI saliency-map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans differ in information and strategy use for explanations, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.

翻译：可解释人工智能（XAI）方法为人工智能模型提供解释，但我们对这些解释与人类解释之间的异同理解仍然有限。在图像分类任务中，我们发现人类在解释时采用了比分类任务本身更具探索性的注意力策略。通过聚类分析识别出两种代表性解释策略：一种策略聚焦于前景物体的视觉扫描，并结合更多用于推断类别标签的概念性解释；另一种则采用探索性扫描，并辅以更多被认为更有效的视觉解释。有趣的是，XAI的显著性图解释与人类探索性注意力策略的相似度最高，而通过扰动方式调用可观察因果关系来突出判别性特征的解释，相比于突出与更高类别分数相关内部特征的解释，与人类策略的相似度更高。因此，人类在解释的信息和策略使用上存在差异，而XAI方法中强调可观察因果关系特征的解释能够更好地匹配人类解释，可能更易于用户理解。